Motor vehicle crashes are the leading cause of death and injury for teens, accounting annually for over 3000 deaths, 100 times as many injuries, and over 14 billion dollars in associated costs. The CDC has identified traffic crashes and associated injuries as a top public health priority. Inexperience is the leading cause of crashes among novice teen drivers, but accumulating the necessary experiences to become a safe driver can take many years. Fortunately, evidence from driving and other domains suggests that it is possible to increase experiential knowledge through scenario-based training. Current methods for providing teens with practice focus on parent-supervised driving. However, parents are ill-prepared to handle this role, with many focusing only on the mechanics of driving, laws and general safety advice. Few parents discuss decision-making aspects of driving and may even pass on inaccurate information. Many parents limit the range of driving situations teens are exposed to mistakenly believing that this increases safety rather than dangerously limiting teens' ability to accumulate important driving experience. Our long-term objective is to design and validate an innovative tool that aims to accelerate the acquisition of critical safety knowledge for teen drivers. We propose to extend our team's past work by developing scenarios appropriate for parents to use to mentor their inexperienced teen drivers. The scenarios will be part of an online educational curriculum designed to help parents provide teens with guided practice as they learn how to drive. The online tool will combine scenario- based training with innovative applications of machine learning technology to evaluate scenario responses and provide tailored feedback containing practical, developmentally appropriate strategies for improving safety as well as recommendations for parent-supervised on-the-road practice. Phase I will address these specific aims: 1) Conduct foundational research to identify realistic scenarios commonly encountered by novice teen drivers through semi-structured interviews with teen drivers aged 15-18. 2) Collect representative responses to scenarios from teens with different levels of driving experience and adults to identify the progression of knowledge and identify developmentally appropriate strategies to use as feedback. 3) Create machine learning algorithms to provide tailored feedback and recommend on-the-road driving experiences based on responses to the scenarios. 4) Develop prototype online system. Phase II will focus on additional specific aims: 5) Develop the Phase I proof of concept into a complete online curriculum with refined algorithms. 6) Evaluate the online curriculum for usability, acceptance via focus groups, and effectiveness via a driving simulator experiment and limited field trial with novice teen drivers. The proposed product represents a significant shift in trainig approaches for teen drivers and through Phase III dissemination it will fill a critical need for evidence- based parent-taught driver's education. PUBLIC HEALTH RELEVANCE: Motor vehicle crashes are the leading cause of death and injury for teens, accounting annually for over 3000 deaths, 100 times as many injuries, and over 14 billion dollars in associated costs; despite this, many teens' primary source of driver's education is their parents. In Phase I we propose to extend our past work for teaching experiential knowledge and develop a prototype online educational curriculum that allows parents to provide teens with guided practice by utilizing innovative machine learning technologies, while Phase II will allow us to complete the prototype and evaluate its effectiveness. The proposed application represents a significant shift in training approaches that has the potential, through Phase III dissemination, to improve teen driving safety.